Create a neural network model using the first eight columns as the key measurements from

the given dataset "diabetic.csv$"$ as input and the final column, which encodes diabetic and

non$-$diabetic patients, as the output vector. The goal is to predict whether a patient is diabetic

based on the given features.

The dataset includes the followings as input and output variables:

$$ Count of pregnancies

$$ Glucose concentration in plasma $2$ hours after an oral glucose tolerance test

$$ Diastolic blood pressure $($in mm Hg$)$

$$ Thickness of triceps skin fold $($in mm$)$

$$ Insulin level in serum $2$ hours after ingestion $($in mu U$/$ml$)$

$$ Body mass index $($weight in kg divided by height in meters squared$)$

$$ Diabetes pedigree function

$$ Age in years

$$ Output: $0$ indicates non$-$diabetic and $1$ indicates diabetic patient